#region License Information
/* HeuristicLab
 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.Instances.DataAnalysis
{
	public class ScalingProblem5 : ArtificialRegressionDataDescriptor
	{

		public override string Name { get { return "JKK-5 F5(X1,X2,X3) = sqrt(X1/log(X2)) + X1/X3"; } }
		public override string Description
		{
			get
			{
				return "Function: F5(X1,X2,X3) = sqrt(X1/log(X2)) + X1/X3" + Environment.NewLine
				+ "Data: 250 points X1, X3 = Rand(1, 2), X2 = Rand(2,100)" + Environment.NewLine
				+ "Function Set: +, -, *, /, square, e^x, e^-x, x^eps, x + eps, x * eps";
			}
		}
		protected override string TargetVariable { get { return "Y"; } }
		protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "Y" }; } }
		protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3" }; } }
		protected override int TrainingPartitionStart { get { return 0; } }
		protected override int TrainingPartitionEnd { get { return 250; } }
		protected override int TestPartitionStart { get { return 250; } }
		protected override int TestPartitionEnd { get { return 1000; } }

		public int Seed { get; private set; }

		public ScalingProblem5() : this((int)DateTime.Now.Ticks) { }

		public ScalingProblem5(int seed) : base()
		{
			Seed = seed;
		}
		protected override List<List<double>> GenerateValues()
		{
			var rand = new MersenneTwister((uint)Seed);

			var data = Enumerable.Range(0, AllowedInputVariables.Count()).Select(_ => Enumerable.Range(0, TestPartitionEnd).Select(__ => rand.NextDouble() + 1).ToList()).ToList();
			data[1] = Enumerable.Range(0, TestPartitionEnd).Select(_ => rand.NextDouble() * 99 + 1).ToList();

			double x1, x2, x3;
			List<double> results = new List<double>();
			for (int i = 0; i < data[0].Count; i++)
			{
				x1 = data[0][i]; 
				x2 = data[1][i];
				x3 = data[2][i];
				results.Add(Math.Sqrt(x1 / (Math.Log(x2))) + (x1 / x3));
			}
			data.Add(results);

			return data;
		}
	}
}